EDUCE: Explaining model Decisions through Unsupervised Concepts Extraction

By Diane Bouchacourt, Ludovic Denoyer. 2019

25 citations recorded by the source corpus.

Providing explanations along with predictions is crucial in some text processing tasks. Therefore, we propose a new self-interpretable model that performs output prediction and simultaneously provides an explanation in terms of the presence of particular concepts in the input. To do so, our model's prediction relies solely on a low-dimensional binary representation of the input, where each feature denotes the presence or absence of concepts. The presence of a concept is decided from an excerpt i.e. a small sequence of consecutive words in the text. Relevant concepts for the prediction task at hand are automatically defined by our model, avoiding the need for concept-level annotations. To ease interpretability, we enforce that for each concept, the corresponding excerpts share similar semantics and are differentiable from each others. We experimentally demonstrate the relevance of our approach on text classification and multi-sentiment analysis tasks.

Subfields: Artificial Intelligence, Information Systems, Safety, Robustness & Interpretability

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Citations in the corpus

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (2019, neutral): “See References [319 –345 • Some methods [272,280] classiﬁed in distinct categories (namely feature relevance for CNN and feature relevance for RNN ) in Fig.”
On Completeness-aware Concept-Based Explanations in Deep Neural Networks (2019, background): “Bouchacourt and Denoyer [8] proposes a Bayesian generative model involving concept vectors.”
Acquisition of chess knowledge in AlphaZero (2021, background): “Concept-based explanations have been successfully used in complex scientific and medical domains, as seen in [20, 21, 22, 23, 24, 25, 26, 27].”
On Concept-Based Explanations in Deep Neural Networks (2019, background): “…that obtain concept representations given human-labeled data on salient concepts, to purely unsupervised approaches that provide concept explanations automatically without human labeling, ranging from k-means clustering of DNN activations [10], to a self-interpretable Bayesian generative model [4].”